Fazan Frederico Sassoli, Brognara Fernanda, Fazan Junior Rubens, Murta Junior Luiz Otavio, Virgilio Silva Luiz Eduardo
Department of Physiology, School of Medicine of Ribeirão Preto, University of São Paulo, Ribeirão Preto, SP 14049-900, Brazil.
Department of Computing and Mathematics, School of Philosophy, Sciences and Languages of Ribeirão Preto, University of São Paulo, Ribeirão Preto, SP 14040-901, Brazil.
Entropy (Basel). 2018 Jan 17;20(1):47. doi: 10.3390/e20010047.
Quantifying complexity from heart rate variability (HRV) series is a challenging task, and multiscale entropy (MSE), along with its variants, has been demonstrated to be one of the most robust approaches to achieve this goal. Although physical training is known to be beneficial, there is little information about the long-term complexity changes induced by the physical conditioning. The present study aimed to quantify the changes in physiological complexity elicited by physical training through multiscale entropy-based complexity measurements. Rats were subject to a protocol of medium intensity training ( n = 13 ) or a sedentary protocol ( n = 12 ). One-hour HRV series were obtained from all conscious rats five days after the experimental protocol. We estimated MSE, multiscale dispersion entropy (MDE) and multiscale SDiff q from HRV series. Multiscale SDiff q is a recent approach that accounts for entropy differences between a given time series and its shuffled dynamics. From SDiff q , three attributes (-attributes) were derived, namely SDiff q m a x , q m a x and q z e r o . MSE, MDE and multiscale -attributes presented similar profiles, except for SDiff q m a x . q m a x showed significant differences between trained and sedentary groups on Time Scales 6 to 20. Results suggest that physical training increases the system complexity and that multiscale -attributes provide valuable information about the physiological complexity.
量化心率变异性（HRV）序列的复杂性是一项具有挑战性的任务，多尺度熵（MSE）及其变体已被证明是实现这一目标最可靠的方法之一。尽管体育锻炼已知有益，但关于体育锻炼引起的长期复杂性变化的信息却很少。本研究旨在通过基于多尺度熵的复杂性测量来量化体育锻炼引起的生理复杂性变化。将大鼠分为中等强度训练组（n = 13）或久坐组（n = 12）。在实验方案实施五天后，从所有清醒的大鼠获取一小时的HRV序列。我们从HRV序列中估计了MSE、多尺度离散熵（MDE）和多尺度SDiff q。多尺度SDiff q是一种新方法，它考虑了给定时间序列与其重排动态之间的熵差异。从SDiff q中导出了三个属性（-属性），即SDiff q max、q max和q zero。除了SDiff q max外，MSE、MDE和多尺度-属性呈现出相似的分布。在时间尺度6到20上，q max在训练组和久坐组之间显示出显著差异。结果表明体育锻炼增加了系统复杂性，并且多尺度-属性提供了有关生理复杂性的有价值信息。
